Abstract
This article proposes and explores a robust approach to identifying differential circulating miRNAs in the plasma of patients with breast cancer. The proposed approach, developed in the framework of the M-estimation, is used to provide protection against potential outliers in miRNA expression data. As the study involves multiple comparisonswith a large number of circulating miRNAs, robust multiple tests are adopted at a given level of false discovery rate (FDR). Also, due to the uncertainties in the underlying distributions of the miRNA expression data sets, the p-values of the multiple tests are approximated using a permutation method. The empirical properties of the proposed robust tests are studied in simulations. An application is provided using miRNA expression data from a breast cancer study.
Highlights
MicroRNAs are short non-coding segments of RNA that are thought to regulate gene expression through sequence-specific base-pairing with target mRNAs (Lee and Ambros, 2001)
As the study involves multiple comparisons with a large number of circulating miRNAs, robust multiple tests are adopted at a given level of false discovery rate (FDR)
The miRNA platform is different from the traditional mRNA gene expression array platform in that the mRNA arrays measure gene expression from specific genes while the miRNA array measures expression of specific miRNAs which represent signaling from many genes
Summary
MicroRNAs (miRNAs) are short non-coding segments of RNA that are thought to regulate gene expression through sequence-specific base-pairing with target mRNAs (Lee and Ambros, 2001). A number of classifiers have been developed for human breast tumors in recent years, including the use of miRNA expression data as prognostic tools. Such tools would be useful for developing blood-based alternative tests for cancer screening and/or diagnosis In this case-control study, a genome-wide miRNA dataset collected during 2009–2010 contained expression levels of miRNAs in the circulation of 20 breast cancer patients and 20 healthy controls using an Illumina miRNA microarray with the expression of 1145 miRNAs. The goal of the study was to identify a unique set of deregulated (differentially expressed) miRNAs that would be associated with having breast cancer. False discovery rates are widely used to deal with false positives that wrongly identify differentially expressed miRNAs. Multiple hypothesis tests based on the classical least squares estimators of location parameters are generally sensitive to potential outliers in the data.
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